Agricultural product cold storage temperature and humidity control method and system

Abstract

The application discloses a kind of agricultural product cold store temperature and humidity control method and system, related to agricultural product cold store temperature and humidity control field, for reasonably control agricultural product cold store temperature and humidity, comprising: obtaining the state data package sent by agricultural product cold store local control system, the receiving time of state data package, state data package includes the temperature and humidity data of cold store, temperature and humidity data generation time stamp and the temperature and humidity control action information executed by local control system;Temperature and humidity data include temperature, humidity;According to generation time stamp and receiving time, the data transmission delay of state data package is calculated;Based on data transmission delay, temperature and humidity data and temperature and humidity control action information, using preset physical model, the temperature and humidity state of cold store at current time is calculated;According to the temperature and humidity state of calculation, load balancing decision is made, and load balancing decision is used to control the temperature and humidity of cold store.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of temperature and humidity control of agricultural product cold storage, and particularly relates to a temperature and humidity control method and system for agricultural product cold storage. BACKGROUND

[0002] In a distributed cold chain network, temperature and humidity control of agricultural product cold storage and realization of network-wide load balancing are the key to ensure the quality of agricultural products and operational efficiency. However, when there are differences in communication link quality in the network, especially when some nodes have significant and uncertain delays in data transmission due to geographical or technical limitations, the node state data received by the central management platform may become outdated.

[0003] Based on such data with different time lags, load balancing decisions may be made, which may result in incorrect control instructions issued by the central platform that do not match the real physical needs of the nodes, thereby causing abnormal temperature and humidity in the cold storage, and even increasing operational costs. More seriously, the system may mistakenly attribute these negative physical feedbacks caused by communication delays to the performance problems of the nodes themselves, thereby forming a self-reinforcing cycle of incorrect judgments. SUMMARY

[0004] The present application provides a temperature and humidity control method for agricultural product cold storage, which is used to reasonably control the temperature and humidity of agricultural product cold storage.

[0005] In a first aspect, to solve the above technical problems, the present application provides a temperature and humidity control method for agricultural product cold storage, comprising: obtaining a state data packet sent by a local control system of the agricultural product cold storage, a receiving time of the state data packet, the state data packet containing temperature and humidity data of the cold storage, a generation time stamp of the temperature and humidity data, and temperature and humidity control action information executed by the local control system; the temperature and humidity data including temperature and humidity; calculating a data transmission delay of the state data packet according to the generation time stamp and the receiving time; based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using a preset physical model to calculate the temperature and humidity state of the cold storage at the current time; making a load balancing decision according to the calculated temperature and humidity state, the load balancing decision being used to control the temperature and humidity of the cold storage.

[0006] Optionally, based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using the preset physical model to calculate the temperature and humidity state of the cold storage at the current time, comprises: determining whether there is a thermodynamic anomaly in the cold storage, the thermodynamic anomaly being used to represent an abnormal increase in heat load of the cold storage; in the case where there is no thermodynamic anomaly in the cold storage, based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using the preset physical model to calculate the temperature and humidity state of the cold storage at the current time.

[0007] Optionally, the determining whether the cold storage exists a thermodynamic abnormality comprises: obtaining type information of goods in the cold storage; determining, according to the type information of the goods, a standard operating power consumption of a refrigeration system required by the cold storage to maintain the goods at a specific temperature; monitoring an actual operating power consumption of the cold storage; determining that the cold storage exists the thermodynamic abnormality in a case where a duration for which the actual operating power consumption is greater than or equal to a preset range of the standard operating power consumption is greater than a preset duration; and determining that the cold storage does not exist the thermodynamic abnormality in a case where the duration for which the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption is less than or equal to the preset duration.

[0008] Optionally, the calculating the temperature and humidity state of the cold storage at the current time instant using the preset physical model comprises: obtaining an actual operating power consumption of the cold storage when the cold storage is in a temperature maintenance operating state; calculating, according to a set temperature of the cold storage, an external environment temperature, and the physical model, an expected operating power consumption required by the cold storage in the temperature maintenance operating state; comparing the actual operating power consumption with the expected operating power consumption; determining that a parameter in the physical model deviates from an actual physical characteristic of the cold storage when the actual operating power consumption continuously exceeds or is equal to a preset deviation range of the expected operating power consumption; updating the parameter in the physical model according to the deviation; and calculating the temperature and humidity state of the cold storage at the current time instant using the updated physical model.

[0009] Optionally, the updating the parameter in the physical model according to the deviation comprises: continuously obtaining a deviation sequence between the actual operating power consumption and the expected operating power consumption; analyzing a dynamic characteristic of the deviation sequence; identifying a main contribution of the deviation according to the dynamic characteristic of the deviation sequence; the main contribution of the deviation comprises at least one of the following: a sudden additional heat load, a cold storage insulation performance attenuation, and a periodic auxiliary heating operation; updating the parameter in the physical model according to the main contribution of the deviation; when the main contribution of the deviation is identified as the cold storage insulation performance attenuation, adjusting a parameter related to the insulation performance in the physical model; when the main contribution of the deviation is identified as the sudden additional heat load, adjusting a parameter related to a heat load of goods in the cold storage in the physical model; and when the main contribution of the deviation is identified as the periodic auxiliary heating operation, suspending the updating of the parameter in the physical model and triggering an abnormality alarm.

[0010] Optionally, the analyzing the dynamic characteristic of the deviation sequence comprises: performing smoothing processing on the deviation sequence to obtain a smoothed deviation sequence; analyzing a trend of the smoothed deviation sequence; identifying an instantaneous fluctuation of the deviation sequence; and judging the dynamic characteristic of the deviation sequence according to the trend and the instantaneous fluctuation.

[0011] Optionally, the main contribution of the deviation is identified, including: analyzing the trend, amplitude and change frequency of the deviation sequence; if the dynamic characteristic indicates that the trend of the deviation sequence is a slow upward trend and the amplitude is within a preset range, it is determined that the main contribution of the deviation is the attenuation of the cold storage insulation performance; if the dynamic characteristic indicates that the amplitude of the deviation sequence changes by more than a change threshold within a preset time length, it is determined that the main contribution of the deviation is the sudden additional heat load; if the dynamic characteristic indicates that the change frequency of the deviation sequence is a periodic change, it is determined that the main contribution of the deviation is the periodic auxiliary heating operation.

[0012] Optionally, according to the main contribution of the deviation, the parameters in the physical model are updated, including: according to the deviation between the actual operating power consumption and the expected operating power consumption, an optimization algorithm is used to perform iterative calculation to obtain an iterative calculation result; according to the iterative calculation result, the parameters in the physical model corresponding to the main contribution of the deviation are adjusted; wherein the parameter adjustment rule is used to define the step size and convergence condition of the optimization algorithm.

[0013] Optionally, the method further comprises: obtaining the amplitude of the deviation between the actual operating power consumption and the expected operating power consumption; adjusting the step size of the optimization algorithm according to the amplitude of the deviation; obtaining the duration or change rate of the deviation between the actual operating power consumption and the expected operating power consumption; defining the convergence condition according to the duration or change rate of the deviation.

[0014] In a second aspect, the present application provides a cold storage temperature and humidity control system for agricultural products, which is used for cold storage temperature and humidity control of agricultural products, and comprises:

[0015] A data packet acquisition module is configured to acquire a state data packet sent by a local control system of the cold storage for agricultural products, a receiving time of the state data packet, and the state data packet containing temperature and humidity data of the cold storage, a generation time stamp of the temperature and humidity data, and temperature and humidity control action information executed by the local control system; the temperature and humidity data includes temperature and humidity;

[0016] A transmission delay calculation module is configured to calculate a data transmission delay of the state data packet according to the generation time stamp and the receiving time;

[0017] A temperature and humidity state estimation module is configured to estimate a temperature and humidity state of the cold storage at a current time based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using a preset physical model;

[0018] A load balancing decision module is configured to make a load balancing decision according to the estimated temperature and humidity state, and the load balancing decision is used for temperature and humidity control of the cold storage.

[0019] Compared with the prior art, the present application has the following beneficial effects:

[0020] The application provides a kind of agricultural product cold storage temperature and humidity control method and system, by compensating data transmission delay and using physical model to calculate the real-time temperature and humidity state of cold storage, to avoid the error decision caused by old data, with the advantages of effectively guaranteeing agricultural product quality and improving operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a kind of agricultural product cold storage temperature and humidity control method flow chart provided by the embodiment of the application;

[0022] Figure 2 It is another kind of agricultural product cold storage temperature and humidity control method flow chart provided by the embodiment of the application;

[0023] Figure 3 It is a kind of agricultural product cold storage temperature and humidity control system structure schematic diagram provided by the embodiment of the application. DETAILED DESCRIPTION

[0024] The technical solutions in the application will be described in detail below with reference to the drawings in the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. The components of the application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0025] It should be noted that: similar signs and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the application, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0026] The agricultural product cold storage temperature and humidity control method provided by the embodiment of the application will be described and explained in detail below through the following specific embodiments.

[0027] Referring to Figure 1 , the application provides an agricultural product cold storage temperature and humidity control method, comprising the following steps:

[0028] S1, obtaining the state data packet sent by the local control system of agricultural product cold storage, the receiving time of the state data packet.

[0029] The state data packet contains temperature and humidity data of the cold storage, a generation timestamp of the temperature and humidity data, and information of temperature and humidity control actions performed by the local control system. The temperature and humidity data includes temperature and humidity. The generation timestamp refers to the recorded time when the temperature and humidity data is generated by the local control system.

[0030] As a possible implementation, the local control system of the agricultural product cold storage can periodically (e.g., every 5 minutes) generate a state data packet and send the data packet to the cold storage temperature and humidity control system. Correspondingly, the cold storage temperature and humidity control system acquires the state data packet sent by the local control system of the agricultural product cold storage. The cold storage temperature and humidity control system can immediately record the current system time to acquire the reception time of the state data packet when receiving the data packet.

[0031] It should be noted that the data packet can be encapsulated in JSON format, which contains fields such as "temperature": 4.5, "humidity": 85, "timestamp": 1678886400 (Unix timestamp, indicating the time when the data is generated), and "control_actions": {"compressor_on": true, "fan_speed": "medium"}.

[0032] In an example, the format of the recorded current system time can be "reception_time": 1678886415.

[0033] S2, according to the generation timestamp and the reception time, calculate the data transmission delay of the state data packet.

[0034] The data transmission delay refers to the time difference between the generation of the state data packet by the local control system and the reception by the central management platform.

[0035] As a possible implementation, the system can calculate the difference between the generation timestamp and the reception time, and determine the difference as the data transmission delay of the state data packet.

[0036] S3, based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, use a preset physical model to calculate the temperature and humidity state of the cold storage at the current time.

[0037] The preset physical model refers to a mathematical or logical model that describes the heat and moisture transfer inside the cold storage and the influence of device operation on temperature and humidity. It can be implemented by using thermodynamic models, heat and mass transfer models, empirical models, or statistical models based on historical data. The purpose is to simulate the dynamic process of the change of cold storage temperature and humidity with time, combine historical data and control actions, and predict the future or current state of the cold storage.

[0038] The temperature and humidity regulation action information refers to operation records of the local control system for adjusting the temperature and humidity of the cold storage, such as start-stop of the refrigeration compressor, operation mode of the fan, start-stop of the dehumidifier, and the like. The temperature and humidity regulation action information is used as an input of the physical model, reflects the influence of the local control on the state of the cold storage, and improves the accuracy of the state estimation.

[0039] As a possible implementation, the system can input the data transmission delay, the temperature and humidity data, and the temperature and humidity regulation action information into a preset physical model. The preset physical model can process the data transmission delay, the temperature and humidity data, and the temperature and humidity regulation action information, and estimate the temperature and humidity state of the cold storage at the current time.

[0040] In an example, the model takes the temperature and humidity data (4.5 degrees and 85% humidity), the data transmission delay of 15 seconds, and the local control action (the compressor is turned on, and the fan is in medium speed) as inputs. The model considers that, within 15 seconds, the temperature of the cold storage will further decrease due to the continuous operation of the compressor, and estimates that the actual temperature of the cold storage at the current time may have decreased to 4.2 degrees and the humidity is 84% in combination with factors such as the breathing heat of the goods in the cold storage. Finally, the load balancing decision module determines whether to assign a new storage task to the cold storage or adjusts the operation strategy of the refrigeration equipment of the cold storage based on the estimated temperature of 4.2 degrees and the humidity of 84%, and in combination with the real-time state of other cold storages, to achieve the energy consumption optimization and load balancing of the entire cold chain network.

[0041] As another possible implementation, to improve the accuracy of the estimation result, the system can determine whether there is a thermodynamic anomaly in the cold storage. In the case where there is no thermodynamic anomaly in the cold storage, the system estimates the temperature and humidity state of the cold storage at the current time based on the data transmission delay, the temperature and humidity data, and the temperature and humidity regulation action information, and by using a preset physical model.

[0042] It should be noted that the thermodynamic anomaly is used to represent an abnormal increase in heat load in the cold storage.

[0043] The abnormal increase in heat load refers to that, under normal operating conditions, due to unplanned events or external environmental changes, the heat entering the cold storage is significantly higher than the heat expected by the physical model. The abnormal increase in heat load can be caused by factors such as high-temperature goods newly stored in the cold storage, the cold storage door being opened for a long time, heat leakage caused by equipment failure, or a sudden rise in external environmental temperature.

[0044] In some preferred embodiments, specifically, to determine whether the cold storage has a thermodynamic anomaly, the instantaneous rate of change of the temperature inside the cold storage can be continuously monitored. For example, if the temperature inside the cold storage rises by more than a preset threshold (e.g., 2°C) within a short period of time (e.g., 5 minutes) and no alarm of failure or shutdown of the refrigeration equipment is received, it can be determined that the cold storage can have a thermodynamic anomaly, which usually indicates an abnormal increase in heat load, such as a large amount of high-temperature goods suddenly entering the cold storage or the door of the cold storage being opened for a long time.

[0045] As a specific implementation, when the system detects that the rate of temperature rise inside the cold storage exceeds 0.5°C per minute and this state lasts more than 3 minutes, the system can determine that the cold storage has a thermodynamic anomaly. In this case, in order to avoid inaccurate calculation results of the physical model in an abnormal state, the system can suspend the temperature and humidity state calculation based on the physical model and trigger an abnormal alarm to prompt the operator to check the cold storage. On the contrary, if the rate of temperature change inside the cold storage remains within the normal range or fluctuates but does not reach the preset abnormal threshold during continuous monitoring, it can be determined that the cold storage does not have a thermodynamic anomaly. In this normal operating state, the system will continue to calculate the temperature and humidity state of the cold storage at the current time based on the data transmission delay, temperature and humidity data, and temperature and humidity control action information obtained from the local control system of the cold storage, using the preset physical model. For example, the system can obtain that the average temperature of the cold storage in the past 10 minutes is 4°C and the humidity is 75%, the data transmission delay is 2 minutes, and the refrigeration equipment runs at 70% power during this period. The physical model will integrate these data, combined with the structural parameters and thermodynamic characteristics of the cold storage, to calculate the actual temperature and humidity inside the cold storage at the current time, for example, the current temperature is 3.8°C and the humidity is 76%. This way ensures that only when the thermodynamic state of the cold storage is stable and predictable, the model-based calculation is performed, thereby ensuring the accuracy of the calculation results.

[0046] It can be understood that due to the possible sudden external heat input during the operation of the cold storage, which is not fully considered by the physical model, such as a large number of newly entered high-temperature agricultural products or a long time opening of the door, these conditions will cause a significant deviation between the actual heat load of the cold storage and the heat load calculated by the physical model based on historical data and preset parameters. If the data in these abnormal conditions is directly input into the physical model for calculation, the model will not accurately capture these external disturbances and will produce inaccurate temperature and humidity state estimation. Therefore, the present scheme first judges whether there is a thermodynamic anomaly in the cold storage, which is used to indicate whether there is an abnormal increase in heat load in the cold storage. Through this judgment, the system can identify whether the current cold storage is in an abnormal thermodynamic state. Only when it is confirmed that the cold storage does not have a thermodynamic anomaly, the system will calculate the temperature and humidity state of the cold storage at the current time based on the data transmission delay, temperature and humidity data and temperature and humidity control action information obtained from the local control system, using the preset physical model. This processing logic ensures that the physical model is only calculated in a relatively stable and predictable thermodynamic environment, thereby avoiding the interference of abnormal heat load on the calculation result. Through the combination of the steps of obtaining data transmission delay, temperature and humidity data and temperature and humidity control action information, the present scheme can more reliably use these real-time data to accurately predict the actual temperature and humidity state of the cold storage under the premise of excluding external interference.

[0047] S4, making a load balancing decision according to the calculated temperature and humidity state.

[0048] The load balancing decision is used for temperature and humidity control of the cold storage.

[0049] As a possible implementation, according to the calculated temperature and humidity state, the system can use a load balancing algorithm (for example, an optimization algorithm based on minimizing the total network energy consumption and balancing equipment wear), considering the current actual load, remaining refrigeration capacity, equipment health, and priority of the incoming task to be allocated of each node, to calculate the optimal resource allocation scheme.

[0050] For example, if it is found after calibration that the temperature of the remote node A has returned to normal and has remaining refrigeration capacity, while the adjacent city node B is idle but far from the new task, the system may decide to allocate the new task to node A, instead of incorrectly allocating it to node B or a more distant node C, thereby avoiding unnecessary transportation costs and risks of goods. Finally, the system will generate specific control instructions (such as "node A compressor maintains current running frequency" or "node A receives new incoming task").

[0051] By the technical solution, the method effectively solves the problem of state data obsolescence caused by communication delay in the distributed agricultural product cold storage network. By accurately calculating the data transmission delay and incorporating it into the cold storage temperature and humidity state calculation process, the system can obtain real-time information closer to the real physical state of the cold storage, overcoming the deviation caused by decision-making based on lagging data in traditional methods. This enables the central management platform to make more accurate and timely load balancing decisions, avoiding the issuance of incorrect control instructions that do not match the actual needs of the cold storage due to distorted information. Therefore, the method can significantly improve the accuracy and response speed of cold storage temperature and humidity control, effectively protect the quality of agricultural products during storage, optimize the overall operation efficiency of the cold chain network, reduce unnecessary energy consumption and operating costs, and fundamentally avoid the negative physical feedback caused by communication delay, which is misinterpreted as a node performance problem, thereby forming a self-reinforcing error judgment cycle.

[0052] In one possible design, as shown in Figure 2 , the system can determine whether there is a thermodynamic anomaly in the cold storage based on the following steps:

[0053] S101, obtain the type information of the goods in the cold storage.

[0054] The type information of the goods refers to the type and characteristic data of the agricultural products or other items stored in the cold storage, such as vegetables, fruits, meat, seafood, etc., as well as their physical properties such as respiratory heat, specific heat capacity, and water content, etc. The purpose is to provide accurate input for subsequent calculation of standard operating power consumption.

[0055] As one possible implementation, when the goods are transported into the cold storage, the system can automatically identify the type information of the goods by scanning the bar code or RFID tag on the packaging of the goods.

[0056] As another possible implementation, the operator can also manually input the main type of goods stored in the cold storage through the cold storage management interface, and then the system can obtain the type information of the goods in the cold storage based on the manual input content.

[0057] S102, determine the standard operating power consumption of the refrigeration system required to maintain the goods at a specific temperature based on the type information of the goods.

[0058] The standard operating power consumption refers to the theoretical or empirical electric energy that the refrigeration system should consume to maintain a specific temperature when storing a specific type of goods in the cold storage.

[0059] As one possible implementation, the system can establish a preset mapping relationship and determine the operating power consumption that has a mapping relationship with the type information of the goods and the specific temperature from the preset mapping relationship, and take the operating power consumption as the standard operating power consumption.

[0060] It should be noted that the preset mapping relationship includes the mapping relationship between the type information of different goods, different temperatures and different standard operating power consumptions.

[0061] S103, monitoring the actual operating power consumption of the cold storage.

[0062] As a possible implementation manner, a smart meter or a power consumption sensor can be arranged on the power supply line of the cold storage, and the system can monitor the actual operating power consumption of the cold storage through the smart meter or the power consumption sensor installed on the power supply line of the cold storage refrigeration system.

[0063] S104, in the case that the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption for a duration greater than a preset duration, it is determined that the cold storage has thermodynamic abnormality.

[0064] The preset range and the preset duration can be set as needed. For example, the preset range can be 10% of the standard operating power consumption. The preset duration can be 10 minutes, etc.

[0065] S105, in the case that the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption for a duration less than or equal to the preset duration, it is determined that the cold storage does not have thermodynamic abnormality.

[0066] In some embodiments, in order to utilize the preset physical model to estimate the temperature and humidity state of the cold storage at the current time, the application further includes the following steps:

[0067] S201, when the cold storage is in a temperature maintenance operating state, obtaining the actual operating power consumption of the cold storage.

[0068] The temperature maintenance operating state refers to that the internal temperature of the cold storage has reached or is close to the set target temperature, and the refrigeration system is mainly in an operating mode for maintaining the temperature, rather than performing substantial temperature reduction or temperature increase. Specifically, it can refer to that the cold storage refrigeration equipment is in an intermittent operation or low-power operation state to offset a small amount of heat load, and the purpose is to provide a stable reference working condition to accurately evaluate the energy consumption performance of the cold storage.

[0069] S202, calculating the expected operating power consumption of the cold storage in the temperature maintenance operating state according to the set temperature of the cold storage, the external environment temperature and the physical model.

[0070] The expected operating power consumption refers to the energy that the cold storage should theoretically consume in the temperature maintenance operating state based on the theoretical physical model of the cold storage under the given set temperature, external environment temperature, etc.

[0071] As a possible implementation manner, the system can input the set temperature of the cold storage and the external environment temperature into the physical model, and the physical model can calculate the expected running power consumption based on preset heat load calculation formula, equipment energy efficiency ratio and other parameters.

[0072] S203, compare the actual running power consumption with the expected running power consumption.

[0073] As a possible implementation manner, the system can periodically (for example, every 5 minutes) read the values of the actual running power consumption and the expected running power consumption and calculate the difference between them, and compare them.

[0074] S204, when the actual running power consumption continuously exceeds or is equal to the preset deviation range of the expected running power consumption, it is determined that the parameters in the physical model deviate from the actual physical characteristics of the cold storage.

[0075] The actual physical characteristics of the cold storage refer to the real physical properties of the cold storage in actual operation, such as its real thermal insulation performance, equipment efficiency, sealing performance, etc. Specifically, it can refer to the actual heat load change caused by factors such as the aging of the cold storage structure, the performance attenuation of the thermal insulation material, and the poor sealing of the door and window, and its purpose is to reflect the performance change that may occur in the long-term operation of the cold storage.

[0076] The deviation refers to the difference between the actual running power consumption and the expected running power consumption, and specifically can refer to the difference between the actual power consumption and the expected power consumption, and its purpose is to quantify the degree of inconsistency between the physical model prediction and the actual performance of the cold storage.

[0077] In an example, when the actual running power consumption exceeds the preset deviation range (for example, set to ±10% of the expected running power consumption) of the expected running power consumption for 30 minutes or more, the system determines that the parameters in the physical model deviate from the actual physical characteristics of the cold storage.

[0078] S205, update the parameters in the physical model according to the deviation.

[0079] As a possible implementation manner, the system can use optimization algorithms such as Kalman filtering or least squares method to iteratively adjust the parameters related to thermal insulation performance and equipment efficiency in the physical model, such as the overall heat transfer coefficient U value of the cold storage or the COP value of the refrigeration equipment, according to the deviation between the actual running power consumption and the expected running power consumption, until the deviation between the model predicted power consumption and the actual power consumption converges within a preset threshold.

[0080] As another possible implementation manner, the system can use an optimization algorithm to obtain an iterative calculation result according to the deviation between the actual running power consumption and the expected running power consumption, and adjust the parameters corresponding to the main contribution of the deviation in the physical model according to the iterative calculation result.

[0081] wherein the step size and the convergence condition of the optimization algorithm are defined according to a parameter adjustment rule. The parameter adjustment rule refers to a set of pre-defined guidelines for guiding the operation of the optimization algorithm and the parameter adjustment process.

[0082] In an example, the system calculates the gradient of the deviation between the actual operating power consumption and the expected operating power consumption under the current physical model parameters, which indicates the direction of parameter adjustment. Then, according to this gradient direction and in combination with the pre-set step size, a small adjustment is made to the insulation performance parameter. For example, if the deviation indicates a decrease in insulation performance, the insulation coefficient can be appropriately increased. This adjustment process is iteratively calculated, and the deviation and gradient are recalculated at each iteration, and the parameter is adjusted again according to the new gradient direction and step size. The parameter adjustment rule can be defined as the initial step size can be set to a small value to ensure the stability of the initial adjustment. As the number of iterations increases, or when the amplitude of the deviation decreases, the step size can be dynamically adjusted, for example, an adaptive step size strategy can be used to automatically reduce the step size when approaching the optimal solution, thereby improving the convergence accuracy. The convergence condition can be defined as when the deviation between the actual operating power consumption and the expected operating power consumption changes by less than a pre-set small threshold after a plurality of consecutive iterations, or when the pre-set maximum number of iterations is reached, the optimization algorithm stops iteration.

[0083] In this way, the insulation performance parameter in the physical model can be accurately adjusted to more accurately reflect the current insulation condition of the cold storage. Similarly, if it is identified that the main contribution to the deviation is the sudden additional heat load, the system can use a similar optimization algorithm to adjust the parameters in the physical model related to the heat load of the goods in the warehouse to adapt to the new heat load situation.

[0084] S206, using the updated physical model, to calculate the temperature and humidity state of the cold storage at the current time.

[0085] As a possible implementation, the system can input the data transmission delay, temperature and humidity data, and temperature and humidity control action information into the updated physical model, and calculate the temperature and humidity state of the cold storage at the current time according to the output result of the updated physical model.

[0086] In some embodiments, the deviation can be the result of the combined action of multiple factors, and simply adjusting the parameters may cause the model to be distorted or the adjustment direction to be incorrect, which cannot quickly converge to the optimal solution.

[0087] To this end, in order to update the parameters in the physical model according to the deviation, the present application further comprises the following steps:

[0088] S301, continuously obtaining a sequence of deviations between the actual operating power consumption and the expected operating power consumption.

[0089] S302, analyze the dynamic characteristics of the deviation sequence.

[0090] The dynamic characteristics of the deviation sequence refer to the law and characteristics of the change of the deviation between the actual operating power consumption and the expected operating power consumption over time, and can specifically include the trend, amplitude, frequency and instantaneous fluctuation of the deviation sequence, and the purpose is to reveal the deep reasons for the deviation by analyzing these characteristics.

[0091] As a possible implementation manner, the system can perform smoothing processing on the deviation sequence to obtain a smoothed deviation sequence, and analyze the trend of the smoothed deviation sequence, identify the instantaneous fluctuation of the deviation sequence, and further judge the dynamic characteristics of the deviation sequence according to the trend and the instantaneous fluctuation.

[0092] For example, the system can use moving average filtering or exponential smoothing method to eliminate random noise in the data to obtain the smoothed deviation sequence. Subsequently, the trend analysis can be performed on the smoothed deviation sequence, for example, by linear regression or time series decomposition to determine its long-term trend, and the instantaneous fluctuation in the deviation sequence is identified, for example, by setting a threshold or using an anomaly detection algorithm to capture the sharp change in a short time. According to these trends and instantaneous fluctuations, the dynamic characteristics of the deviation sequence can be determined.

[0093] S303, identify the main contribution of the deviation according to the dynamic characteristics of the deviation sequence.

[0094] The main contribution of the deviation includes at least one of the following: sudden additional thermal load, cold storage insulation performance attenuation, periodic auxiliary heating operation.

[0095] As a possible implementation manner, the system can analyze the trend, amplitude and change frequency of the deviation sequence to identify the main contribution of the deviation, which can be divided into the following three cases:

[0096] If the dynamic characteristics indicate that the trend of the deviation sequence is a slow rising trend and the amplitude is within a preset range, it is determined that the main contribution of the deviation is the cold storage insulation performance attenuation.

[0097] If the dynamic characteristics indicate that the amplitude of the deviation sequence changes by more than a change threshold within a preset time length, it is determined that the main contribution of the deviation is the sudden additional thermal load.

[0098] If the dynamic characteristics indicate that the change frequency of the deviation sequence is periodic change, it is determined that the main contribution of the deviation is the periodic auxiliary heating operation.

[0099] S304, update the parameters in the physical model according to the main contribution of the deviation.

[0100] As a possible implementation manner, the system can adjust the parameter related to the insulation performance in the physical model when identifying that the main contribution of the deviation is the insulation performance degradation of the cold storage; adjust the parameter related to the heat load of the goods in the cold storage in the physical model when identifying that the main contribution of the deviation is the sudden additional heat load; and suspend the update of the parameter in the physical model and trigger an abnormal alarm when identifying that the main contribution of the deviation is the periodic auxiliary heating operation.

[0101] For example, when identifying the insulation performance degradation, the heat transfer coefficient in the model can be adjusted; and when identifying the sudden additional heat load, the specific heat capacity or heat release rate of the goods in the model can be adjusted.

[0102] Since the unreasonable convergence condition can cause the algorithm to stop iteration too early, the optimal parameter adjustment result cannot be obtained, or the iteration time is too long, and the calculation burden is increased.

[0103] In this regard, the application further proposes that the step of adjusting the parameter corresponding to the main contribution of the deviation in the physical model comprises:

[0104] S401, obtaining the amplitude of the deviation between the actual operation power consumption and the expected operation power consumption.

[0105] The amplitude of the deviation refers to the absolute value of the difference between the actual operation power consumption and the expected operation consumption, and the purpose is to quantify the deviation degree of the model prediction from the actual situation.

[0106] As a possible implementation manner, the system can calculate the absolute value of the difference between the actual operation power consumption and the expected operation power consumption, and take the absolute value as the amplitude of the deviation between the actual operation power consumption and the expected operation power consumption.

[0107] S402, adjusting the step length of the optimization algorithm according to the amplitude of the deviation.

[0108] The step length of the optimization algorithm refers to the adjustment amount of each parameter update in the iteration process, which can be determined by using a fixed numerical value, a dynamic adjustment factor based on gradient information or a preset lookup table, and the purpose is to control the speed and stability of the parameter update.

[0109] As a possible implementation manner, the system can determine the step length corresponding to the amplitude of the deviation from the preset lookup table according to the amplitude of the deviation, so as to adjust the step length of the optimization algorithm.

[0110] The preset lookup table includes the mapping relationship between different amplitudes of the deviation and different step lengths.

[0111] In an example, the preset lookup table defines three intervals: small deviation (0-5%), medium deviation (5%-15%) and large deviation (>15%). When a large deviation is detected, the system can adjust the step size of the optimization algorithm, for example, set it to a larger value, such as 0.1; when a medium deviation is detected, the step size can be set to a medium value, such as 0.05; when a small deviation is detected, the step size can be set to a smaller value, such as 0.01. This segmented adjustment strategy can ensure fast convergence when the deviation is large and fine adjustment when the deviation is small.

[0112] S403, obtain the duration or change rate of the deviation between the actual operating power consumption and the expected operating power consumption.

[0113] As a possible implementation, the system can determine the duration by recording the length of time during which the deviation value continuously exceeds a certain threshold, or determine the change rate by calculating the difference between the deviation values at consecutive time points.

[0114] S404, define the convergence condition according to the duration or change rate of the deviation.

[0115] As a possible implementation, when the deviation duration is long and the change rate is small, the convergence condition can be appropriately relaxed to allow the algorithm to iterate more fully to obtain more accurate parameters. When the deviation change rate is large, the convergence condition can be tightened to prompt the algorithm to converge as soon as possible to cope with rapidly changing cold storage operating environments.

[0116] In an example, assuming that when the deviation duration exceeds 30 minutes and the change rate is less than 0.01% / minute, it indicates that the deviation is relatively stable, at which time the convergence condition can be defined as a change of the objective function value of less than 0.001 for 5 consecutive iterations, allowing the algorithm to iterate more fully. When the deviation change rate is greater than 0.05% / minute, it indicates that the deviation fluctuates sharply, at which time the convergence condition can be tightened, for example, defined as a change of the objective function value of less than 0.01 for 2 consecutive iterations, prompting the algorithm to converge quickly to adapt to dynamic changes.

[0117] Through the above technical solutions, the step size of the optimization algorithm can be adaptively adjusted according to the magnitude of the deviation between the actual operating power consumption and the expected operating power consumption, avoiding the problem of algorithm oscillation or slow convergence speed caused by fixed step size. At the same time, the convergence condition can be dynamically defined according to the duration or change rate of the deviation, ensuring that the algorithm can stop iterating with appropriate accuracy and efficiency under different operating conditions, avoiding the problem of stopping too early or iterating for too long. This makes the adjustment process of the physical model parameters more efficient and accurate, improves the adaptability of the model to the actual physical characteristics of the cold storage, and further improves the accuracy of temperature and humidity calculation and the precision and stability of cold storage temperature and humidity control.

[0118] AsFigure 3 The embodiment of the present application also provides a cold storage temperature and humidity control system for agricultural products. The system comprises:

[0119] a data packet acquisition module, configured to acquire a state data packet sent by a local control system of the cold storage for agricultural products, a receiving time of the state data packet, the state data packet containing temperature and humidity data of the cold storage, a generation time stamp of the temperature and humidity data and information of a temperature and humidity control action performed by the local control system; the temperature and humidity data comprising temperature and humidity;

[0120] a transmission delay calculation module, configured to calculate a data transmission delay of the state data packet according to the generation time stamp and the receiving time;

[0121] a temperature and humidity state calculation module, configured to calculate a temperature and humidity state of the cold storage at a current time based on the data transmission delay, the temperature and humidity data and the information of the temperature and humidity control action, by using a preset physical model;

[0122] a load balancing decision module, configured to make a load balancing decision according to the calculated temperature and humidity state, the load balancing decision being used for temperature and humidity control of the cold storage.

[0123] The embodiment of the present application also provides a terminal device. The terminal device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, for example, a cold storage temperature and humidity control program for agricultural products. The processor implements the steps in the above-mentioned various cold storage temperature and humidity control methods for agricultural products when executing the computer program. Alternatively, the processor implements the functions of the modules / units in the above-mentioned various systems when executing the computer program.

[0124] For example, the computer program can be divided into one or more modules / units, the one or more modules / units are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0125] The terminal device can be a desktop computer, a notebook computer, a palm computer, a smart tablet and the like. The terminal device can comprise, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above-mentioned components are only examples of the terminal device and do not constitute a limitation on the terminal device, and the terminal device can comprise more or fewer components than the above-mentioned components, or combine certain components or different components, for example, the terminal device can also comprise an input / output device, a network access device, a bus and the like.

[0126] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0127] The memory can be used to store computer programs and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the terminal device (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0128] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include any entity or system capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0129] It should be noted that the above-described system embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0130] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for controlling temperature and humidity in a cold storage facility for agricultural products, characterized in that, Includes the following steps: The system acquires a status data packet sent by the local control system of the agricultural product cold storage, the time of receipt of the status data packet, and the status data packet containing temperature and humidity data of the cold storage, the generation timestamp of the temperature and humidity data, and information on the temperature and humidity control actions performed by the local control system; the temperature and humidity data includes temperature and humidity. Calculate the data transmission delay of the status data packet based on the generation timestamp and the reception time; Based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, the temperature and humidity status of the cold storage at the current moment is calculated using a preset physical model. Based on the calculated temperature and humidity conditions, a load balancing decision is made, which is used to control the temperature and humidity of the cold storage. The step of using a preset physical model to calculate the temperature and humidity status of the cold storage at the current moment includes: When the cold storage is in temperature maintenance operation mode, the actual operating power consumption of the cold storage is obtained; Based on the set temperature of the cold storage, the external ambient temperature, and the physical model, calculate the expected operating power consumption required for the cold storage to maintain operation at the specified temperature. Compare the actual operating power consumption with the expected operating power consumption; When the actual operating power consumption is continuously greater than or equal to the preset deviation range of the expected operating power consumption, it is determined that the parameters in the physical model deviate from the actual physical characteristics of the cold storage. Update the parameters in the physical model based on the deviation; Using the updated physical model, the temperature and humidity status of the cold storage at the current moment is calculated.

2. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 1, characterized in that, The step of calculating the temperature and humidity status of the cold storage at the current moment based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using a preset physical model, includes: Determine whether the cold storage has a thermodynamic anomaly, which is used to characterize an abnormal increase in heat load in the cold storage; If the cold storage does not exhibit the aforementioned thermodynamic anomaly, the temperature and humidity status of the cold storage at the current moment is calculated using a preset physical model based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information.

3. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 2, characterized in that, Determining whether the cold storage has a thermodynamic anomaly includes: Obtain information about the type of goods in the cold storage; Based on the type information of the goods, determine the standard operating power consumption of the refrigeration system required by the cold storage to maintain the goods at a specific temperature; Monitor the actual operating power consumption of the cold storage; If the duration of the actual operating power consumption being greater than or equal to the preset range of the standard operating power consumption is greater than the preset duration, it is determined that the cold storage has a thermodynamic anomaly. If the duration for which the actual operating power consumption is greater than or equal to the preset range of the standard operating power consumption is less than or equal to the preset duration, it is determined that the cold storage does not have a thermodynamic abnormality.

4. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 1, characterized in that, The step of updating the parameters in the physical model based on the deviation includes: Continuously acquire the deviation sequence between the actual operating power consumption and the expected operating power consumption; Analyze the dynamic characteristics of the deviation sequence; Based on the dynamic characteristics of the deviation sequence, identify the main contributors to the deviation; the main contributors to the deviation include at least one of the following: sudden additional heat load, cold storage insulation performance degradation, and periodic auxiliary heating operation; Update the parameters in the physical model based on the main contribution of the deviation; Specifically, when the main contributor to the deviation is identified as the degradation of the cold storage insulation performance, the parameters related to insulation performance in the physical model are adjusted; when the main contributor to the deviation is identified as a sudden additional heat load, the parameters related to the heat load of the goods in the storage are adjusted; when the main contributor to the deviation is identified as periodic auxiliary heating operations, the updating of the physical model parameters is paused and an anomaly alarm is triggered.

5. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 4, characterized in that, The analysis of the dynamic characteristics of the deviation sequence includes: The deviation sequence is smoothed to obtain a smoothed deviation sequence; Analyze the trend of the deviation sequence after the smoothing process; Identify the instantaneous fluctuations of the deviation sequence; Based on the trend and the instantaneous fluctuations, the dynamic characteristics of the deviation sequence are determined.

6. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 5, characterized in that, The main contributions to identifying the bias include: Analyze the trend, magnitude, and frequency of change of the deviation sequence; If the dynamic characteristics indicate that the trend of the deviation sequence is a slow upward trend and the amplitude is within a preset range, then the main contribution of the deviation is determined to be the degradation of the cold storage insulation performance. If the dynamic characteristics indicate that the amplitude change of the deviation sequence within a preset time period is greater than the change threshold, then the main contribution of the deviation is determined to be a sudden additional heat load. If the dynamic characteristics indicate that the frequency of change of the deviation sequence is periodic, then the main contribution of the deviation is determined to be the periodic auxiliary heating operation.

7. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 6, characterized in that, The step of updating the parameters in the physical model based on the main contribution of the deviation includes: Based on the deviation between the actual operating power consumption and the expected operating power consumption, an optimization algorithm is used to perform iterative calculations to obtain the iterative calculation results; Based on the iterative calculation results, the parameters in the physical model corresponding to the main contributions of the deviation are adjusted; The step size and convergence condition of the optimization algorithm are defined according to the parameter adjustment rules.

8. The method for controlling temperature and humidity in a cold storage for agricultural products according to claim 7, characterized in that, The method further includes: Obtain the magnitude of the deviation between the actual operating power consumption and the expected operating power consumption; The step size of the optimization algorithm is adjusted according to the magnitude of the deviation. The duration or rate of change of the deviation between the actual operating power consumption and the expected operating power consumption is obtained; The convergence condition is defined based on the duration or rate of change of the deviation.

9. A temperature and humidity control system for agricultural product cold storage, used for temperature and humidity control in agricultural product cold storage, characterized in that, The system includes: The data packet acquisition module is used to acquire status data packets sent by the local control system of the agricultural product cold storage, the reception time of the status data packets, the status data packets including the temperature and humidity data of the cold storage, the generation timestamp of the temperature and humidity data, and the temperature and humidity control action information performed by the local control system; the temperature and humidity data includes temperature and humidity. A transmission delay calculation module is used to calculate the data transmission delay of the status data packet based on the generation timestamp and the reception time; The temperature and humidity state estimation module is used to estimate the temperature and humidity state of the cold storage at the current moment based on the data transmission delay, the temperature and humidity data, and the temperature and humidity control action information, using a preset physical model. The load balancing decision module is used to make load balancing decisions based on the calculated temperature and humidity conditions. The load balancing decisions are used to control the temperature and humidity of the cold storage. The data packet acquisition module is used to acquire the actual operating power consumption of the cold storage when it is in temperature maintenance operation mode; Based on the set temperature of the cold storage, the external ambient temperature, and the physical model, calculate the expected operating power consumption required for the cold storage to maintain operation at the specified temperature. Compare the actual operating power consumption with the expected operating power consumption; When the actual operating power consumption is continuously greater than or equal to the preset deviation range of the expected operating power consumption, it is determined that the parameters in the physical model deviate from the actual physical characteristics of the cold storage. Update the parameters in the physical model based on the deviation; Using the updated physical model, the temperature and humidity status of the cold storage at the current moment is calculated.

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